1. Image is NOT Perfect Sometimes
EE465: Introduction to Digital Image Processing

2. Image Enhancement for Visually Impaired Patients
EE465: Introduction to Digital Image Processing

3. EE465: Introduction to Digital Image Processing
Image Enhancement
Introduction
Spatial domain techniques
Point operations
Histogram equalization and matching
Applications of histogram-based enhancement
Frequency domain techniques
Unsharp masking
Homomorphic filtering*
EE465: Introduction to Digital Image Processing

4. EE465: Introduction to Digital Image Processing
Recall:
There is no boundary of imagination in the virtual world
In addition to geometric transformation (warping) techniques, we can also photometrically transform images
Ad-hoc tools: point operations
Systematic tools: histogram-based methods
Applications: repair under-exposed or over-exposed photos, increase the contrast of iris images to facilitate recognition, enhance microarray images to facilitate segmentation.
EE465: Introduction to Digital Image Processing

5. Point Operations Overview
Point operations are zero-memory operations where
a given gray level x[0,L] is mapped to another
gray level y[0,L] according to a transformation y L x L
L=255: for grayscale images
EE465: Introduction to Digital Image Processing

6. EE465: Introduction to Digital Image Processing
Lazy Man Operation y L x L
No influence on visual quality at all
EE465: Introduction to Digital Image Processing

7. EE465: Introduction to Digital Image Processing
Digital Negative L x L
EE465: Introduction to Digital Image Processing

8. EE465: Introduction to Digital Image Processing
Contrast Stretching yb ya x a b L
EE465: Introduction to Digital Image Processing

9. EE465: Introduction to Digital Image Processing
Clipping x a b L
EE465: Introduction to Digital Image Processing

10. EE465: Introduction to Digital Image Processing
Range Compression x L
c=100
EE465: Introduction to Digital Image Processing

11. Summary of Point Operation
So far, we have discussed various forms of mapping function f(x) that leads to different enhancement results
MATLAB function >imadjust
The natural question is: How to select an appropriate f(x) for an arbitrary image?
One systematic solution is based on the histogram information of an image
Histogram equalization and specification
EE465: Introduction to Digital Image Processing

12. Histogram based Enhancement
Histogram of an image represents the relative frequency
of occurrence of various gray levels in the image
MATLAB function >imhist(x)
EE465: Introduction to Digital Image Processing

13. EE465: Introduction to Digital Image Processing
Why Histogram?
It is a baby in the cradle!
Histogram information reveals that image is under-exposed
EE465: Introduction to Digital Image Processing

14. EE465: Introduction to Digital Image Processing
Another Example
Over-exposed image
EE465: Introduction to Digital Image Processing

15. EE465: Introduction to Digital Image Processing
How to Adjust the Image?
Histogram equalization
Basic idea: find a map f(x) such that the histogram of the modified (equalized) image is flat (uniform).
Key motivation: cumulative probability function (cdf) of a random variable approximates a uniform distribution
Suppose h(t) is the histogram (pdf)
EE465: Introduction to Digital Image Processing

16. Histogram Equalization
Uniform
Quantization
Note: y
cumulative probability function L 1 x L
EE465: Introduction to Digital Image Processing

17. MATLAB Implementation
function y=hist_eq(x)
[M,N]=size(x);
for i=1:256
h(i)=sum(sum(x= =i-1));
End
y=x;s=sum(h);
I=find(x= =i-1);
y(I)=sum(h(1:i))/s*255;
end
Calculate the histogram
of the input image
Perform histogram
equalization
EE465: Introduction to Digital Image Processing

18. EE465: Introduction to Digital Image Processing
Image Example
before
after
EE465: Introduction to Digital Image Processing

19. EE465: Introduction to Digital Image Processing
Histogram Comparison
before equalization
after equalization
EE465: Introduction to Digital Image Processing

20. Adaptive Histogram Equalization
EE465: Introduction to Digital Image Processing

21. Histogram Specification/Matching
Given a target image B, how to modify a given image A such that
the histogram of the modified A can match that of target image B?
histogram1
histogram2
S-1*T T S ?
EE565 Advanced Image Processing Copyright Xin Li 2008

22. Application (I): Digital Photography
EE465: Introduction to Digital Image Processing

23. Application (II): Iris Recognition
before
after
EE465: Introduction to Digital Image Processing

24. Application (III): Microarray Techniques
before
after
EE465: Introduction to Digital Image Processing

25. EE465: Introduction to Digital Image Processing
Application (IV)
EE465: Introduction to Digital Image Processing

26. Application (V): Entertainment
Adaptive histogram
-equalized1
Histogram-equalized
LDR video
1Eric P. Bennett and Leonard McMillan. “Video enhancement using per-pixel virtual exposures,” In ACM SIGGRAPH 2005
EE465: Introduction to Digital Image Processing

27. EE465: Introduction to Digital Image Processing
Image Enhancement
Introduction
Spatial domain techniques
Point operations
Histogram equalization and matching
Applications of histogram-based enhancement
Frequency domain techniques
Unsharp masking
Homomorphic filtering*
EE465: Introduction to Digital Image Processing

28. Frequency-Domain Techniques (I): Unsharp Masking
g(m,n) is a high-pass filtered version of x(m,n)
• Example (Laplacian operator)
EE465: Introduction to Digital Image Processing

29. MATLAB Implementation
% Implementation of Unsharp masking
function y=unsharp_masking(x,lambda)
% Laplacian operation
h=[0 -1 0; ;0 -1 0]/4;
dx=filter2(h,x);
y=x+lambda*dx;
EE465: Introduction to Digital Image Processing

30. EE465: Introduction to Digital Image Processing
1D Example
xlp(n)
x(n)
g(n)=x(n)-xlp(n)
EE465: Introduction to Digital Image Processing

31. EE465: Introduction to Digital Image Processing
2D Example
MATLAB command
>roidemo
EE465: Introduction to Digital Image Processing

32. Frequency-Domain Techniques (II): Homomorphic filtering
Basic idea:
Illumination
(low freq.)
reflectance
(high freq.)
freq. domain enhancement
EE465: Introduction to Digital Image Processing

33. EE465: Introduction to Digital Image Processing
Image Example
before
after
EE465: Introduction to Digital Image Processing

34. Summary of Nonlinear Image Enhancement
Understand how image degradation occurs first
Play detective: look at histogram distribution, noise statistics, frequency-domain coefficients…
Model image degradation mathematically and try inverse-engineering
Visual quality is often the simplest way of evaluating the effectiveness, but it will be more desirable to measure the performance at a system level
Iris recognition: ROC curve of overall system
Microarray: ground-truth of microarray image segmentation result provided by biologists
EE465: Introduction to Digital Image Processing